Summary of
Predictive Analytics

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Rating

8Overall

8
Importance

7
Innovation

8
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Recommendation

Predictive analytics (PA) is a concept that’s both undeniably powerful and potentially creepy. This branch of computer science combines big data with statistics to foretell what you might buy, how you might vote or when you might die. Author and predictive analytics guru Eric Siegel is an unabashed cheerleader for his discipline, and he mostly brushes off the privacy concerns it raises. But that’s not to dismiss his study, which is engagingly written and elegantly translates dense materials and abstract concepts into easy-to-read prose. Siegel draws on a number of real-world examples from well-known companies, including Target, Hewlett-Packard and Chase Bank, and he describes his own experience as an expert consultant on predictive analytics. The result is an enlightening, plainly written guide. getAbstract recommends his PA overview to managers and investors seeking insight into a fast-growing corner of the tech economy.

In this summary, you will learn

How the popularity of predictive analytics exploded,

How organizations use predictions and

How decision trees work.

About the Author

Former Columbia University professor Eric Siegel is the founder of Predictive Analytics World and executive editor of The Predictive Analytics Times.

Summary

Big Data and Predictions

Imagine it’s 2022. Predictive analytics (PA) plays a crucial role in your life, starting with your commute to work. When you get in your car, a predictive model will use biometrics to verify your identity. Spotify will pick music based on its forecast of your musical tastes.

Comment on this summary

Good introduction. It didn't answer the question posted at beginning on how to *handle* privacy concerns.

TimADMIN Aulph
1 year ago

This summary was a great read! Although I am not heavily involved with Analytics in my current position, analyzing data has always intrigued me. After reading this summary, I realize why - the predictive analytics side of it! I especially like the part about Decision Trees: 'Decision trees are “simple, elegant and precise” and “practically mathless.”' Math was one of my favorite subjects growing up; however, if I can do something more simply and get good results out of it, I will go with that option over using complex data with statistical formulas when and where I can! :)